Setup
pacman::p_load("tidyverse", "here", "glue", "colorspace", "gsheet", "labelled", "sf", "pdftools")
Daten
Tile Grid Map
grid <- read_csv(here("data", "de_constituencies_grid.csv"))
Vorhersage der Direktmandatsgewinner*innen 2021
Source: Kayser, Leininger, Murr & Stötzer (2021) Citizens’ Forecast for the 2021 German National Election https://aleininger.eu/citizens_forecast2021/
sheet_url <- "https://docs.google.com/spreadsheets/d/1xOg9kNRMfmUXoJAYNp7R93UHfPlVus-2VV-i-Rdx1Uc/edit#gid=0"
dat_erst <- gsheet2tbl(sheet_url)
#dat_erst <- read_csv(here("data", "Buerger_innenvorhersage 2021 - Prognose.csv"))
#' t-Test auf Basis von Mittelwert und Standardabweichung
t_test_from_summary <- function(m1, m2, sd1, sd2, n1, n2, ...) {
group1 <- scale(1:n1)*sd1 + m1
group2 <- scale(1:n2)*sd2 + m2
t.test(group1, group2, ...)
}
dat_erst_ttest <- dat_erst %>%
mutate(Stimmenanteil_Mean = ifelse(Stimmenanteil_Mean > 1000,
Stimmenanteil_Mean / 1000,
Stimmenanteil_Mean)) %>%
group_by(wkr) %>%
slice_max(order_by = Stimmenanteil_Mean, n = 2, with_ties = FALSE) %>%
mutate(rank = rank(-Stimmenanteil_Mean, ties.method = "first")) %>%
ungroup() %>%
select(wkr, rank, Stimmenanteil_Mean, Stimmenanteil_SD, obs) %>%
pivot_wider(id_cols = wkr,
names_from = "rank",
values_from = c("Stimmenanteil_Mean", "Stimmenanteil_SD", "obs")) %>%
mutate(t_test = pmap(list(m1 = Stimmenanteil_Mean_1, m2 = Stimmenanteil_Mean_2,
sd1 = Stimmenanteil_SD_1, sd2 = Stimmenanteil_SD_2,
n1 = obs_1, n2 = obs_2, alternative = "greater"),
t_test_from_summary),
t = map_dbl(t_test, "statistic"),
p_value = map_dbl(t_test, "p.value"))
dat_erst_winneronly_dw <-
dat_erst %>%
group_by(wkr) %>%
dplyr::select(wkr, Wahlkreisname, party, kandidate_name, obs, Gewinner_share) %>%
mutate(Gewinner_share = ifelse(Gewinner_share > 100, Gewinner_share / 1000, Gewinner_share),
rank = rank(Gewinner_share, ties.method = "first"),
name = to_character(wkr),
Gewinner_share = round(Gewinner_share*100)) %>%
filter(rank > 5) %>%
mutate(party = as.character(party)) %>%
unite(val, party, Gewinner_share, kandidate_name) %>%
spread(rank,val) %>%
separate("7",into = c("first-place-party", "first-place-votes", "winner"),"_") %>%
separate("6",into = c("second-place-party", "second-place-votes", "second"),"_") %>%
mutate(outcome = ifelse(`first-place-votes` == `second-place-votes`, 'Kopf-an-Kopf', `first-place-party`)) %>%
relocate("outcome", "first-place-party", "first-place-votes", "winner", .before = "second-place-party") %>%
ungroup() %>%
# add t-test statistic
inner_join(dat_erst_ttest, by = "wkr")
Kombinierte Datensätze
dat_winneronly_grid <-
dat_erst_winneronly_dw %>%
left_join(grid, by = c("Wahlkreisname" = "wk_full")) %>%
mutate(
outcome_agg = ifelse(outcome %in% c("CDU", "CSU"), "CDU/CSU", outcome),
diff = as.numeric(`first-place-votes`) - as.numeric(`second-place-votes`),
outcome_agg = factor(outcome_agg, levels = c("CDU/CSU", "SPD", "Grüne", "Linke", "AfD", "Kopf-an-Kopf"))
)
Visualisierungen
Setup
theme_set(theme_void(base_size = 16, base_family = "Editorial New")) #Editorial New, Noto Serif
theme_update(legend.margin = margin(0, 0, 0, 25),
legend.text = element_text(margin = margin(5, 0, 5, 0)),
plot.title = element_text(hjust = .5, face = "bold",
lineheight = 1.1, margin = margin(t = 10, b = 20)),
plot.subtitle = element_text(hjust = .5, color = "grey40", size = 15,
margin = margin(t = -8, b = 18)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0, color = "grey40",
lineheight = 1.3,
size = 9, margin = margin(20, 0, 5, 0)),
plot.caption.position = "plot",
plot.margin = margin(10, 0, 10, 0))
# Party colors
party_colors <- c("CDU/CSU" = "grey9",
#"CSU" = "grey18",
"SPD" = "#ca0002", ## "#E3000F", a bit darker now to make it work with CVD
"Grüne" = rgb(100, 161, 45, maxColorValue = 255),
#"FDP" = darken("#ffed00", 0.1),
"Linke" = "purple",
"AfD" = rgb(0, 158, 224, maxColorValue = 255))
caption_de <- "Grafik: Cédric Scherer & Ansgar Wolsing\nDaten: Kayser, Leininger, Murr & Stötzer (2021) Citizens’ Forecast for the 2021 German National Election (aleininger.eu/citizens_forecast2021)\nDie Karte basiert auf einer Befragung einer nicht-repräsentativen Stichprobe von Bürger*innen in allen 299 Wahlkreisen zu den Erfolgsaussichten des*der Kandidierenden*in."
title_de <- "Bürger*innenvorhersage der Direktmandatsgewinner*innen\nin den Wahlkreisen zur Bundestagswahl 2021"
caption_en <- "Graphic: Cédric Scherer & Ansgar Wolsing\nData: Kayser, Leininger, Murr & Stötzer (2021) Citizens’ Forecast for the 2021 German National Election (aleininger.eu/citizens_forecast2021)\nThe map is based on a survey of a non-representative sample of citizens in all 299 Bundestag constituencies on the candidates' chances of success."
title_en <- "Citizens’ Forecast of Direct Mandate Winners in\nthe Constituencies for the 2021 Federal Election"
parties_en <- c("CDU/CSU", "SPD", "The Greens", "The Left", "AFD", "Too close to call")
Tile Grid Map
g <- ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(aes(color = outcome_agg), size = 12, shape = 15) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL) +
guides(color = guide_legend(override.aes = list(size = 6))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en) +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_en.pdf"), width = 10, height = 13, device = cairo_pdf)
Variante mit gedämpfteren Farben für die Füllung der Punkte
g <-
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg),
size = 11.5, shape = 22, stroke = 1.5, alpha = .5, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 11.5, shape = 22, stroke = 1.5, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
guides(color = guide_legend(override.aes = list(size = 6))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_de_v2.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en) +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_en_v2.pdf"), width = 10, height = 13, device = cairo_pdf)
Bubble Map
g <- ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(aes(color = outcome_agg), size = 10) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL) +
guides(color = guide_legend(override.aes = list(size = 6))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en) +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_en.pdf"), width = 10, height = 13, device = cairo_pdf)
Variante mit gedämpfteren Farben für die Füllung der Punkte
g <-
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg),
size = 10, shape = 21, stroke = 2, alpha = .5, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 10, shape = 21, stroke = 2, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
guides(color = guide_legend(override.aes = list(size = 6))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_de_v2.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en) +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_en_v2.pdf"), width = 10, height = 13, device = cairo_pdf)
Grid Map inkl. Vorsprung als Farbintensität
Vorsprung in Prozentpunkten
g <-
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg, alpha = diff),
size = 11.5, shape = 22, stroke = 1.5, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 11.5, shape = 22, stroke = 1.5, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_diff_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en,
subtitle = "The more intense the coloring of the dots, the greater the predicted lead.") +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_grid_map_diff_en.pdf"), width = 10, height = 13, device = cairo_pdf)
Bubble Map inkl. Vorsprung als Farbintensität
Vorsprung in Prozentpunkten
g <-
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg, alpha = diff),
size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 9, shape = 21, stroke = 2, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = caption_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_diff_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = caption_en,
subtitle = "The more intense the coloring of the dots, the greater the predicted lead.") +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_diff_en.pdf"), width = 10, height = 13, device = cairo_pdf)
Vorsprung als t-Statistik
g <-
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg,
# alpha = diff
alpha = t # Ergebnis t-Test (oder p-value stattdessen?)
),
size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 9, shape = 21, stroke = 2, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9))) +
theme(legend.position = c(.87, .25))
## Deutsche Version
g + labs(title = title_de, caption = glue("{caption_de}\nDie Vorhersage der Mandatsgewinner basiert auf einem t-Test auf Basis von Mittelwert und Standardabweichung."),
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_t_test_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en, caption = glue("{caption_en}\nThe prediction of mandate winners is based on a t-test based on mean and standard deviation."),
subtitle = "The more intense the coloring, the greater the predicted lead.") +
scale_color_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Too close to call" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_t_test_en.pdf"), width = 10, height = 13, device = cairo_pdf)
Bubble Map inkl. Grenzen der Bundesländer (trial)
Leider nicht so schön und sinnvoll wie erhofft.
ggplot(dat_winneronly_grid, aes(col, row)) +
ggforce::geom_mark_hull(
aes(group = bundesland_de),
color = "white",
expand = unit(0, "mm")
) +
geom_point(aes(color = outcome_agg), size = 7) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL) +
guides(color = guide_legend(override.aes = list(size = 6))) +
theme(legend.position = c(.87, .25),
plot.background = element_rect(color = "grey67", fill = "grey67")) +
labs(title = title_de, caption = caption_de)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_states.pdf"), width = 10, height = 13, device = cairo_pdf)
Bubble Map mit verschiedenen Punktgrößen für Bundesländer (trial)
Weder effektiv noch schön.
# Größenkategorien für Bundesländer
bland_group_mapping <- c(
"Schleswig-Holstein" = "A",
"Mecklenburg-Vorpommern" = "B",
"Hamburg" = "D",
"Niedersachsen" = "C",
"Bremen" = "E",
"Brandenburg" = "A",
"Sachsen-Anhalt" = "D",
"Berlin" = "A",
"Nordrhein-Westfalen" = "D",
"Sachsen" = "A",
"Hessen" = "E",
"Thüringen" = "B",
"Rheinland-Pfalz" = "A",
"Bayern" = "D",
"Baden-Württemberg" = "B",
"Saarland" = "A"
)
dat_winneronly_grid %>%
mutate(mark_group = bland_group_mapping[bundesland_de],
mark_shape = case_when(
mark_group == "A" ~ 21,
mark_group == "B" ~ 22,
mark_group == "C" ~ 23,
mark_group == "D" ~ 24,
mark_group == "E" ~ 25,
)) %>%
ggplot(aes(col, row)) +
geom_point(
aes(fill = outcome_agg, alpha = diff, size = mark_group),
# size = 9,
shape = 21,
stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg, size = mark_group),
# size = 9,
shape = 21,
stroke = 2, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
scale_size_discrete(range = c(7, 11)) +
# scale_shape_identity() +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9)),
size = "none") +
theme(legend.position = c(.87, .25)) +
labs(title = title_de, caption = caption_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_diff_bland_mark.pdf"), width = 10, height = 13, device = cairo_pdf)
Bubble Map inkl. Vorsprung als Farbintensität - mit Städten zur Orientierung
constituencies_to_highlight <- c(
18, # Hamburg-Mitte
75, # Berlin-Mitte
93, # Köln I
220 # München-West/Mitte
)
df_constituencies_highlight <- dat_winneronly_grid %>%
filter(wkr %in% constituencies_to_highlight) %>%
mutate(name_short = str_extract(Wahlkreisname, "[a-zA-ZäöüÄÖÜ]+")) %>%
select(name_short, name, col, row)
Städtenamen als Overlay
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_point(
aes(fill = outcome_agg, alpha = diff),
size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 9, shape = 21, stroke = 2, fill = "transparent"
) +
geom_label(data = df_constituencies_highlight,
aes(label = name_short),
col = "grey99",
fill = "grey10", alpha = 0.4, family = "Roboto",
label.size = 0,
fontface = "bold", size = 6,
hjust = 0.5) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9))) +
theme(legend.position = c(.87, .25)) +
labs(title = title_de, caption = caption_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_diff_location_highlights.pdf"), width = 10, height = 13, device = cairo_pdf)
Städtenamen als Labels
labels <- dat_winneronly_grid %>%
filter(str_detect(wk, "München|Köln") | bundesland_de %in% c("Berlin", "Hamburg")) %>%
filter(wk != "München-Land") %>%
mutate(label = case_when(
str_detect(wk, "München") ~ "München",
str_detect(wk, "Köln") ~ "Köln",
TRUE ~ bundesland_de
))
ggplot(dat_winneronly_grid, aes(col, row)) +
geom_tile(
data = labels,
fill = "grey60", color = "grey60"
) +
geom_point(
fill = "white", size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(fill = outcome_agg, alpha = diff),
size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 9, shape = 21, stroke = 2, fill = "transparent"
) +
# ggforce::geom_mark_hull(
# data = labels,
# aes(group = label),
# concavity = 0
# ) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 6, alpha = .9))) +
theme(legend.position = c(.87, .25)) +
labs(title = title_de, caption = caption_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.")
ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_diff_location_highlights_ggforce.pdf"), width = 10, height = 13, device = cairo_pdf)
Interaktive Version via {ggiraph}
library(ggiraph)
g_interactive <- dat_winneronly_grid %>%
mutate(label = str_wrap(glue("{Wahlkreisname} ({bundesland_de})<br><br>Vorsprung:<br>{outcome} {diff}%"), 50)) %>%
ggplot(aes(col, row)) +
geom_point_interactive(
aes(fill = outcome_agg, alpha = diff, tooltip = label, data_id = label),
size = 9, shape = 21, stroke = 2, color = "transparent"
) +
geom_point(
aes(color = outcome_agg),
size = 9, shape = 21, stroke = 2, fill = "transparent"
) +
coord_fixed() +
scale_x_continuous(limits = c(-.5, max(grid$col) + 2)) +
scale_y_reverse(expand = c(.03, .03)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(color = guide_legend(override.aes = list(size = 6))) +
labs(title = title_de, caption = caption_de)
tooltip_css <- "background-color:#515151;color:white;font-family:Roboto;padding:10px;border-radius:5px;"
girafe(ggobj = g_interactive,
width_svg = 12, height_svg = 12,
options = list(
opts_sizing(rescale = FALSE),
opts_tooltip(offx = 50, css = tooltip_css)
))
#ggsave(here("plots", "citizen_forecast", "citizen_forecast_bubble_map_states.pdf"), width = 10, height = 13, device = cairo_pdf)
Hexagon Tiles
## READ GEOMETRY ==============================================
#' https://pitchinteractiveinc.github.io/tilegrams/
#' Download geometry "Germany - Constituencies" as TopoJSON
#' and place it in the data directory
filepath_topo <- here("data", "tiles.topo.json")
wk_topo <- geojsonio::topojson_read(filepath_topo)
wk_topo <- wk_topo %>% mutate(id = as.numeric(id))
# Merge shapes of constituencies into state-level shapes
bland_shape <- wk_topo %>%
inner_join(dat_winneronly_grid, by = c("id" = "wkr")) %>%
group_by(bundesland_de) %>%
summarize(geometry = st_union(geometry))
df_constituencies_highlight_hex <- wk_topo %>%
filter(id %in% constituencies_to_highlight) %>%
mutate(geometry = st_make_valid(geometry) %>%
st_centroid(),
lon = map(geometry, 1),
lat = map(geometry, 2),
name_short = str_extract(name, "[a-zA-ZäöüÄÖÜ]+"))
g <- dat_winneronly_grid %>%
inner_join(wk_topo, by = c("wkr" = "id")) %>%
ggplot(aes(geometry = geometry)) +
geom_sf(aes(fill = outcome_agg, col = outcome_agg, alpha = diff),
size = 0.1, # col = "grey80"
) +
geom_sf_text(aes(label = id),
size = 2.5, family = "Roboto Mono") +
geom_sf(data = bland_shape,
aes(geometry = geometry,
group = bundesland_de),
fill = NA, col = "grey96",
size = 2, show.legend = FALSE) +
geom_sf(aes(col = outcome_agg),
size = 0.1, fill = "transparent"
) +
# geom_sf_label(data = df_constituencies_highlight_hex,
# aes(lon, lat, label = name_short),
# col = "grey99",
# fill = "grey10", alpha = 0.4, family = "Roboto",
# label.size = 0,
# fontface = "bold", size = 6,
# hjust = 0.5) +
#scale_x_continuous(limits = c(NA, )) +
scale_y_continuous(expand = c(.01, .01)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 0.25, alpha = .5))) +
theme(legend.position = c(.08, .15),
legend.key.size = unit(5, "mm"))
## Deutsche Version
g + labs(title = title_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.",
caption = glue("{caption_de}\nTilegram-Geometrie: pitchinteractiveinc.github.io"))
ggsave(here("plots", "citizen_forecast", "citizen_forecast_hexagon_map_diff_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en,
subtitle = "The more intense the coloring of the dots, the greater the predicted lead.",
caption = glue("{caption_en}\nTilegram-Geometrie: pitchinteractiveinc.github.io")) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_hexagon_map_diff_en.pdf"), width = 10, height = 13, device = cairo_pdf)
g <- dat_winneronly_grid %>%
inner_join(wk_topo, by = c("wkr" = "id")) %>%
ggplot(aes(geometry = geometry)) +
geom_sf(aes(fill = outcome_agg, col = outcome_agg, alpha = t),
size = 0.1, # col = "grey80"
) +
geom_sf_text(aes(label = id),
size = 2.5, family = "Roboto Mono") +
geom_sf(data = bland_shape,
aes(geometry = geometry,
group = bundesland_de),
fill = NA, col = "grey96",
size = 2, show.legend = FALSE) +
geom_sf(aes(col = outcome_agg),
size = 0.1, fill = "transparent"
) +
# geom_sf_label(data = df_constituencies_highlight_hex,
# aes(lon, lat, label = name_short),
# col = "grey99",
# fill = "grey10", alpha = 0.4, family = "Roboto",
# label.size = 0,
# fontface = "bold", size = 6,
# hjust = 0.5) +
#scale_x_continuous(limits = c(NA, )) +
scale_y_continuous(expand = c(.01, .01)) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")), name = NULL) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "transparent")), name = NULL) +
scale_alpha(range = c(0, .86), guide = "none") +
guides(fill = guide_legend(override.aes = list(size = 0.25, alpha = .5))) +
theme(legend.position = c(.08, .15),
legend.key.size = unit(5, "mm"))
## Deutsche Version
g + labs(title = title_de,
subtitle = "Je intensiver die Färbung, desto größer ist der vorhergesagte Vorsprung.",
caption = glue("{caption_de}\nTilegram-Geometrie: pitchinteractiveinc.github.io"))
ggsave(here("plots", "citizen_forecast", "citizen_forecast_hexagon_map_t_test_de.pdf"), width = 10, height = 13, device = cairo_pdf)
## Englische Version
g + labs(title = title_en,
subtitle = "The more intense the coloring of the dots, the greater the predicted lead.",
caption = glue("{caption_en}\nTilegram-Geometrie: pitchinteractiveinc.github.io")) +
scale_color_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL, labels = parties_en) +
scale_fill_manual(values = c(party_colors, c("Kopf-an-Kopf" = "grey85")),
name = NULL, labels = parties_en)
ggsave(here("plots", "citizen_forecast", "citizen_forecast_hexagon_map_t_test_en.pdf"), width = 10, height = 13, device = cairo_pdf)
pdfs <- list.files(here(), pattern = "*.pdf", recursive = TRUE)
for(pdf in pdfs) {
pdf_convert(pdf = glue("{here()}/{pdf}"),
filenames = glue("{here()}/{str_remove(pdf, '.pdf')}.png"),
format = "png", dpi = 500)
}
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Sys.time()
[1] "2021-09-27 14:44:16 CEST"R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
system code page: 65001
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] ggiraph_0.7.10 pdftools_3.0.1 sf_1.0-2
[4] labelled_2.8.0 gsheet_0.4.5 colorspace_2.0-2
[7] glue_1.4.2 here_1.0.1 forcats_0.5.1
[10] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4
[16] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] ellipsis_0.3.2 class_7.3-19 rprojroot_2.0.2
[4] fs_1.5.0 rstudioapi_0.13 httpcode_0.3.0
[7] proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[10] fansi_0.5.0 lubridate_1.7.10 xml2_1.3.2
[13] downlit_0.2.1 knitr_1.34 jsonlite_1.7.2
[16] broom_0.7.9 dbplyr_2.1.1 rgeos_0.5-8
[19] compiler_4.1.0 httr_1.4.2 backports_1.2.1
[22] assertthat_0.2.1 fastmap_1.1.0 lazyeval_0.2.2
[25] cli_3.0.0 htmltools_0.5.2 tools_4.1.0
[28] gtable_0.3.0 geojson_0.3.4 V8_3.4.2
[31] Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.4
[34] vctrs_0.3.8 crul_1.1.0 xfun_0.26
[37] rvest_1.0.1 lifecycle_1.0.1 pacman_0.5.1
[40] jqr_1.2.1 scales_1.1.1 vroom_1.5.5
[43] ragg_1.1.3 hms_1.1.1 parallel_4.1.0
[46] yaml_2.2.1 curl_4.3.2 sass_0.4.0
[49] distill_1.2 stringi_1.7.4 highr_0.9
[52] maptools_1.1-2 e1071_1.7-9 rlang_0.4.11
[55] pkgconfig_2.0.3 systemfonts_1.0.2 evaluate_0.14
[58] lattice_0.20-44 htmlwidgets_1.5.4 labeling_0.4.2
[61] bit_4.0.4 tidyselect_1.1.1 magrittr_2.0.1
[64] geojsonsf_2.0.1 R6_2.5.1 geojsonio_0.9.4
[67] generics_0.1.0 DBI_1.1.1 foreign_0.8-81
[70] pillar_1.6.3 haven_2.4.3 withr_2.4.2
[73] units_0.7-2 sp_1.4-5 modelr_0.1.8
[76] crayon_1.4.1 uuid_0.1-4 KernSmooth_2.23-20
[79] utf8_1.2.2 tzdb_0.1.2 rmarkdown_2.11
[82] grid_4.1.0 readxl_1.3.1 qpdf_1.1
[85] reprex_2.0.1 digest_0.6.28 classInt_0.4-3
[88] textshaping_0.3.5 munsell_0.5.0 bslib_0.3.0
[91] askpass_1.1